Index of papers in Proc. ACL 2009 that mention
  • co-occurrence
Yang, Qiang and Chen, Yuqiang and Xue, Gui-Rong and Dai, Wenyuan and Yu, Yong
Image Clustering with Annotated Auxiliary Data
Intuitively, our algorithm aPLSA performs PLSA analysis on the target images, which are converted to an image instance-to-feature co-occurrence matrix.
Image Clustering with Annotated Auxiliary Data
At the same time, PLSA is also applied to the annotated image data from social Web, which is converted into a text-to-image-feature co-occurrence matrix.
Image Clustering with Annotated Auxiliary Data
Based on the image data set V, we can estimate an image instance-to-feature co-occurrence matrix AMXIJEl 6 WWW, where each element Ala-(1 g i g |V| and l g j g |.7-"|) in the matrix A is the frequency of the feature fj appearing in the instance 21,-.
co-occurrence is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Yang, Hui and Callan, Jamie
Experiments
Co-occurrence 0.47 0.56 0.45 0.41 0.41
Experiments
Co-occurrence 0.34 0.36 0.34 0.31 0.31
Experiments
Both co-occurrence and lexico-syntactic patterns work well for all three types of relations.
Introduction
The common types of features include contextual (Lin, 1998), co-occurrence (Yang and Callan, 2008), and syntactic dependency (Pantel and Lin, 2002; Pantel and Ravichandran, 2004).
Introduction
The framework integrates contextual, co-occurrence , syntactic dependency, lexi-cal-syntactic patterns, and other features to learn an ontology metric, a score indicating semantic distance, for each pair of terms in a taxonomy; it then incrementally clusters terms based on their ontology metric scores.
Related Work
Inspired by the conjunction and appositive structures, Riloff and Shepherd (1997), Roark and Charniak (1998) used co-occurrence statistics in local context to discover sibling relations.
Related Work
Besides contextual features, the vectors can also be represented by verb-noun relations (Pereira et al., 1993), syntactic dependency (Pantel and Ravichandran, 2004; Snow et al., 2005), co-occurrence (Yang and Callan, 2008), conjunction and appositive features (Caraballo, 1999).
The Features
The features include contextual, co-occurrence , syntactic dependency, lexical-syntactic patterns, and miscellaneous.
The Features
The second set of features is co-occurrence .
The Features
In our work, co-occurrence is measured by point-wise mutual information between two terms:
co-occurrence is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Kim, Jungi and Li, Jin-Ji and Lee, Jong-Hyeok
Term Weighting and Sentiment Analysis
Statistical measures of associations between terms include estimations by the co-occurrence in the whole collection, such as Point-wise Mutual Information (PMI) and Latent Semantic Analysis (LSA).
Term Weighting and Sentiment Analysis
Another way is to use co-occurrence statistics
Term Weighting and Sentiment Analysis
where K is the maximum window size for the co-occurrence and is arbitrarily set to 3 in our experiments.
co-occurrence is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
McIntyre, Neil and Lapata, Mirella
Experimental Setup
The second one creates a story randomly without taking any co-occurrence frequency into account.
Introduction
Our generator operates over predicate-argument and predicate-predicate co-occurrence statistics gathered from corpora.
Story Ranking
As explained earlier, our generator produces stories stochastically, by relying on co-occurrence frequencies collected from the training corpus.
The Story Generator
A fragment of the action graph is shown in Figure 3 (for simplicity, the edges in the example are weighted with co-occurrence frequencies).
co-occurrence is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Huang, Jian and Taylor, Sarah M. and Smith, Jonathan L. and Fotiadis, Konstantinos A. and Giles, C. Lee
Experiments
To decide whether two names in the co-occurrence or family relationship match, we use the SoftTFIDF measure (Cohen et al., 2003), which is a hybrid matching scheme that combines the token-based TFIDF with the Jam-Winkler string distance metric.
Methods 2.1 Document Level and Profile Based CDC
For instance, the similarity between the occupations ‘President’ and ‘Commander in Chief’ can be computed using the JC semantic distance (J iang and Conrath, 1997) with WordNet; the similarity of co-occurrence with other people can be measured by the J accard coefficient.
Methods 2.1 Document Level and Profile Based CDC
a match in a family relationship is considered more important than in a co-occurrence relationship.
co-occurrence is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: